Distributed Development Challenges The DevOps model is not specific to distributed software development, but the principles and practices behind DevOps are useful concepts for managing common pitfalls of the distributed development model. Advantages of Distributed Software Development In , MIT Sloan Management Review published a study on the performance of 80 distributed software development teams, including teams working in different cities, countries, or continents.
Distributed Development Best Practices Best practices for distributed development teams include creating meaningful virtual interactions , having face-to-face onboarding and training, and using tools to measure and manage distributed development standards. Create onboarding documentation that establishes your distributed development standards and workflow cadence, and include resources, such as a contact sheet with phone numbers for their team and the organization.
Video Validation: Face-to-face meetings are productive culture-building activities for distributed development teams. Integrate more face-to-face virtual interactions with distributed employees, especially at the start of a project or employment.
Incorporate video meetings for the first week of training and consider co-located, in-person training for the first 30 days of work, if possible.
Mix up your sessions by including team-building activities designed to create more casual interactions and provide a platform for teams to get to know each other in a different setting. For examples of top team-building activities for remote teams , read this article. Measured Meritocracy: Distributed software development requires buy-in from everyone on the team, so create shared goals.
Coordinate accurate and attainable timelines for distributed development teams and celebrate meeting development milestones. Distribute work equally, and measure production with metrics that determine if your distributed teams are meeting business needs.
Eliminate the Grapevine: Be transparent and share data with the entire team using asynchronous, easy-to-access, and explicit communication channels. Incentivize collaboration and reward transparency when a project is off track or a problem is blocking a team. To learn more about how to effectively communicate with a remote team, read this article.
Distributed Development Collaboration Tools Collaborative software and technology is integral to open-source development and the culture of creating software with teams dispersed across different organizations, locations, and time zones. Collaboration Tools Help Scale Open-Source Pandemic Response Helpful Engineering is a fully distributed, global team with thousands of diverse volunteer engineers publishing documentation in GitHub, which is an open-source version-control platform compatible with the distributed software development model.
Distributed Communication and Documentation Coordinating the flow of information across dispersed teams is crucial to empowering distributed collaboration. Communication Is a Currency for Distributed Volunteers Chowdhury believes communication is the most important practice when building and managing a distributed software development project.
Outsourcing Distributed Development Outsourcing is distinct from distributed development. The following can also present challenges to outsourcing software development tasks to distributed teams in different geo-locations: Managing cultural differences for example, task-related goals and relationship-related goals Coordinating workflow overlap and communicating with time delays Compromising on in-house collaboration on task-related development processes, such as code sharing and software testing.
Become a Time Zone Master Working across time zones adds new organizational challenges to day-to-day tasks. As a manager, you can combat burnout by establishing core work and collaboration hours with each team member based on their time zone: Collect, share, and update these hours with everyone in your team agreement documentation — especially when onboarding and training new team members.
Use a structured time overlap schedule to coordinate meetings and workflow across different time zones. Strive for the best possible schedule overlap that incorporates a significant percentage of your team based on availability. Tools like the hTime Global Clock and web-based time zone calculators help plan overlap schedules. Have a system for asynchronous collaboration to account for workflow and communication outside of the overlap window. It enables you to provide the needed assistance and to get the project done on time.
The most successful companies allocate budget for offline events: dinners, some outdoor activities, and meetings. Such events offer great opportunities to establish personal connections between the team members. It, in turn, allows them feeling themselves more like a team. Want to know more about outsourcing software development? Read our full software development outsourcing guide. There are many things to consider when switching to distributed software development. Such a team offers many advantages, the most important of which are an opportunity to quickly fill the tech talent gap and cost-efficiency.
However, there are some cons as well. They are mostly connected with the remote character of the team and are connected with the establishing of communication and personal connections among the team members and making each member feel involved in each project stage.
Since , our company provides dedicated teams for software development to companies from all over the world. We have developed our own strategies and ways to manage distributed teams to keep all the members motivated and engaged. Choosing a dedicated engineering team at Relevant, you may be confident that we will perform optimally and provide the expected results for our clients. Contact us to build a successful distributed software development team.
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Andrew Burak. How to Build a Successful Distributed Software Development Team Dedicated teams Modern technologies allow companies to use tech talents globally and build a successful distributed development team. Table of Contents. Tags: dedicated team. My company has helped hundreds of companies scale engineering teams and build software products from scratch.
And trust cannot be built between people unless people connect in person and spend meaningful time together. In this situation, there is a very high risk of negative feedback being given or taken in the wrong spirit.
This is an important challenge when teams are distributed and have a high level of dependencies between them. Imagine a day when a team in Pune India leaves behind a broken Build as the other team in San Francisco starts the workday.
This will result in loss of productivity for the team in San Francisco. Even if there is every reason to believe that this was an inadvertent slip from the India team, it could cause resentment in the US team, thereby leading to an increase in the trust deficit. While working from a remote location, it is quite difficult to get good visibility of work happening in other locations, as radiation of information across locations is a huge challenge.
This is typically seen at offshore locations, when onshore has a superiority complex. When onshore team members carry the belief that the work done by the offshore team is relatively of low value as compared to their work, they seldom appreciate the other team members.
This can lead to a feeling of being taken for granted and result in low morale. Collective code ownership means that no single member of the team owns a piece of code, it is owned by the entire team. This means that the code is up for refactoring to all team members. Implementing this in a distributed environment poses two major challenges.
Distributed agreement problems The various failure scenarios in distributed systems, and transmission delays in particular, have instigated important work on the foundations of distributed software. Much of this work has focussed on the central issue of distributed agreement. There are many variations of this problem, including time synchronization, consistent distributed state, distributed mutual exclusion, distributed transaction commit, distributed termination, distributed election, and so on.
However, all of these reduce to the common problem of reaching agreement in a distributed environment in the presence of failures. We introduce this problem with the following apocryphal story. Consider the case of two army generals of ancient times, when communication between distant sites could only be achieved by physically carrying messages between the sites. The two generals, Amphiloheus and Basileus, are in a predicament whereby the enemy host lies between them.
While neither has the strength to single-handedly defeat the enemy, their combined force is sufficient. Thus, they must commence their attack at precisely the same time or they risk being defeated in turn.
Their problem then is to agree on a time of attack. Unfortunately for them, a messenger going from one general to the other must pass through enemy lines with a high likelihood of being caught. Assume then, that Amphiloheus sends his messenger to Basileus with a proposed time of attack.
To ensure that the message was received, he demands that the messenger return with a confirmation from Basileus. While this is going on, Basileus could be in the process of sending his own messenger with his proposal-possibly different-for the time of the attack.
The problem is obvious: if the messenger fails to get back to Amphiloheus, what conclusions can be reached? If the messenger succeeded in reaching the other side but was intercepted on the way back, there is a possibility that Basileus will attack at the proposed time but not Amphiloheus since he did not get a confirmation.
However, if the messenger was caught before he reached Basileus, then Amphiloheus is in danger of acting alone and suffering defeat. Furthermore, even if the messenger succeeds in getting back to Amphiloheus, there is still a possibility that.
Basileus will not attack, because he is unsure that his confirmation actually got through. To remedy this, Basileus may decide to send his own messenger to Amphiloheus to ensure that his confirmation got through. But, the only way he can be certain of that is if he gets a confirmation of his confirmation.
Since there is a possibility that neither messenger got through to Amphiloheus, Basileus is no better off than before if his second messenger does not return. Clearly, while sending additional messengers can increase the likelihood that a confirmation will get through, it does not fundamentally solve the problem since there will always be a finite probability that messengers will get intercepted. Impossibility result Our parable of the generals is simply an illustration of a fundamental impossibility result.
Namely, it has been formally proven that it is not possible to guarantee that two or more distributed sites will reach agreement in finite time over an asynchronous 3 communication medium, if the medium between them is lossy [7] or if one of the distributed sites can fail [2]. This important result is, unfortunately, little known and many distributed system developers are still trying to solve what is known to be an unsolvable problem-the modern-day equivalent of trying to square the circle or devise a perpetual motion machine.
The best that we can do in these circumstances is to reduce the possibility of non-termination to something that is highly unlikely. The Byzantine generals problemA common paradigm for a particular form of the distributed agreement problem is the so-called Byzantine generals problem.
Furthermore, any faulty processing sites are assumed to be malicious in the sense that they are trying to subvert agreement by intentionally sending incorrect information.
While the number of such sites is known, their identity is not. The objective is to find an algorithm whereby all the non-faulty sites can reach agreement despite the disruptive actions of the faulty sites. This may seem like a rather exotic and needlessly paranoid problem of little practical value. However, it is actually quite important for fault-tolerant systems. The point is that any solution that can survive a malicious attack will be robust enough to survive practically any type of fault.
Heterogeneity Many distributed systems arose out of the need to integrate legacy stand-alone software systems into a larger more comprehensive system. Since the individual systems were often developed independently of each other, they may have been based on different architectural principles and even different programming and operating system technologies.
This creates another common problem of distribution: integration of heterogeneous technologies. For example, one system might use a bit integer format with the most significant bit in the leftmost position, while its peer may use a bit format with the most significant bit in the rightmost position.
System establishment Since there are multiple processing sites in a distributed system, there are multiple loci of control. Note that, in general, it cannot be assumed that all the sites will necessarily start executing at the same time. For example, a site that failed and was then recovered will come up long after all the other components are all operational. A major problem is how these distributed sites find and synchronize with each other.
Distributed algorithms and techniques In this section we examine some of the most common distributed algorithms. These algorithms generally are based on shared common knowledge [7] ; that is, each site must know what all the other sites know. Consequently, an important related problem is how to disseminate distributed knowledge reliably and efficiently. A major difficulty with common knowledge is that all parties have to know about each other. In dynamic systems, where sites may come and go, this can add a significant amount of overhead.
In particular, difficulties result when a new site joins while a distributed algorithm is in progress. In those cases, it is typical to refuse entry to the newcomer until the algorithm terminates. Conversely, a site may fail in the middle of executing a distributed algorithm, which means that all the other participants must be notified.
Communication techniques Both synchronous and asynchronous communication is used in distributed systems. Synchronous communication, in the form of remote procedure call or rendezvous, has the advantage of combining synchronization with communication.
Once a synchronous invocation completes, the invoking party knows positively whether the communication has succeeded and, if it has, it also knows something about the state of the invoked site. For this reason, synchronous distributed communication primitives are directly supported in many distributed languages such as Ada [10] and occam [12].
The principle is simple. When the rendezvous occurs, the invoking site is suspended until the invoked operation is completed at the remote site and a reply is received. The receiving site simply continues its execution. Unfortunately, synchronous communication typically involves significant overhead mainly because of the need to deal with failures that reduces efficiency.
Furthermore, while a synchronous communication is in progress, both participants are unreceptive to any other communications. In many reactive and real-time systems, such prolonged periods of autism are unacceptable. In contrast, asynchronous communication, or message passing, is simpler and more efficient, but does leave the problem of synchronization to the application.
Messages are simply sent to the receiver regardless of whether the receiver is ready to accept them. The Byzantine generals algorithm In the Byzantine generals problem, the objective is for all the non-faulty sites to agree on a value, even if faulty sites are sending incorrect information.
Furthermore, we require that the agreed on value must be based on the information received from all other non-faulty sites. The latter is stipulated to prevent agreement on an arbitrary value but to represent a true consensus.
The problem and a solution were first described by Lamport, Shostak, and Pease. Clearly, considering our previous discussions on the difficulty of achieving these conditions in distributed systems, these are rather significant constraints on the applicability of the algorithm. They are introduced to simplify the problem. Variants of the algorithm have been developed that relax some of these conditions. In the Lamport-Shostak-Pease algorithm, one of the sites initiates the agreement process.
We refer to this site as the coordinator while the other sites are called followers.
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