You must have heard parents teach their younger children that sharing is caring. Well, let’s just modify this a little bit to make it more relevant to our story: dynamic sharing is caring! But what is this all about!? Read on to find out!

We’re sure you’ve heard from that one friend about how powerful their shiny new computer is. Terms like processor, cores and multicore serve all but one purpose; to indicate speed and power of the system. However, the flaunt of a powerful system on paper renders useless when the distress of underperformance ensues. This is where the focus and attention shift from cosmetics of a product to its underlying ‘tech underbelly’, where the balance between performance and energy efficiency is the name of the game. One of the challenges in a computing system is to manage its cache effectively, so as to minimize energy utilization without affecting too much performance. Under the guidance of Dr. Muhammad Adeel Pasha (PhD advisor),Dr. Saad Zia Sheikh’s work focuses on these underlying details of a computing system.

Dr. Saad’s work mainly deals with management of shared caches, specifically devising a holistic solution for cache-aware system-level energy minimization while ensuring the schedulability for periodic tasks. Additionally, his work proposes a dynamic cache-partition schedulability analysis for multicore partitioned scheduling. This work hopes to manage the shared cache problem while also optimizing the energy consumed by the entire system.

We extend our heartiest congratulations to Dr. Saad Zia and his PhD advisor, Dr. Muhammad Adeel Pasha for this work. The following is a list of Dr. Saad’s published work.

Journal Papers:

• S. Z. Sheikh and M. A. Pasha, "Energy-Efficient Multicore Scheduling for Hard RealTime Systems: A Survey," 2018, ACM Transactions in Embedded Computing Systems (TECS) 17, 6, Article 94, 26 pages. DOI: https://doi.org/10.1145/3291387
• S. Z. Sheikh and M. A. Pasha, " Energy-efficient Real-time Scheduling on Multicores: A Novel Approach to Model Cache Contention,” 2020, ACM Transactions in Embedded Computing Systems (TECS). 19, 4, Article 28 ,25 pages. DOI: https://doi.org/10.1145/3399413
• S. Z. Sheikh and M. A. Pasha, "Cache-Aware Energy-Efficient Scheduling on Heterogeneous Multicores" IEEE Transactions in Parallel and Distributed Systems (TPDS)
• [Under Review]
• S. Z. Sheikh and M. A. Pasha, "Dynamic Cache-Partition Schedulability Analysis for Partitioned Scheduling on Multicore Real-Time Systems. IEEE Letters of Computer Society (LOCS) [Early Access Available: https://ieeexplore.ieee.org/document/9162466]

Conference Papers:

• S. Z. Sheikh and M. A. Pasha, "An Improved Model for System-Level Energy Minimization on Real-Time Systems," 2019, IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Rennes, FR, 2019, pp. 276-282. DOI: 10.1109/MASCOTS.2019.00039 [https://ieeexplore.ieee.org/document/8843101]

First author of a groundbreaking paper in the Proceedings of the IEEE, Dr. Hassan Jaleel’s work describes optimal solutions to complex problems linked with multirobot architectures.

Like swarms of sardines, swimming in a magnificently choreographed movement, robots in the future may need to form functional clusters that are adaptable to a multitude of requirements ranging from transport to mapping and surveys, exploration and communication.

One of the areas of interest for Dr. Jaleel’s study is task allocation and optimizing an underlying objective for multirobot systems. His recent study discovered a connection between coverage problems and task assignment. Like sardines in sea, multirobot systems may need to learn how to effectively perform decision-making using local data, to evade a pursuing target, for example. To deal with similar scenarios, Dr. Hassan Jaleel, along with his collaborator Dr. Jeff Shamma, took a jab at the multirobot problem using two approaches.

First, local decisions and global optimisation are seen from the framework of convex optimisation which is a well-established tool in locating most suitable solutions to complex problems. Second, the task of optimisation is linked with recurrent incidents of learning, framed in the language of games, where under prescribed rules and multiple players the best configuration is achieved by repetitive learning process between players.

The hope is that this work can serve large scale societal purpose and sets the stage for applying control and game theory to multiple scenarios. We congratulate Dr. Hassan Jaleel on having his paper published in this prestigious journal.

Citation

H. Jaleel and J. S. Shamma, "Distributed Optimisation for Robot Networks: From Real-Time Convex Optimisatiofn to Game-Theoretic Self-Organisation," in Proceedings of the IEEE, vol. 108, no. 11, pp. 1953-1967, Nov. 2020, doi: 10.1109/JPROC.2020.3028295.