Contention in shared caches caused by concurrently executing applications can lead to overall performance degradation in multiprocessor systems-on-chip (MPSoCs). To address this issue, various shared cache arbitration techniques have been proposed to manage cache bandwidth contention. These techniques focus on enhancing overall system performance; however, this optimization often comes at the expense of system fairness, leading to some applications experiencing disproportionate slowdowns, or, in the worst case, starvation. Therefore, an effective shared cache bandwidth management policy is needed to optimize performance while ensuring fairness across applications. We propose FARRE , a novel fairness aware request-response arbitration technique for shared caches. FARRE is designed to optimize performance while attempting to maintain a user-defined fairness threshold. We evaluate its effectiveness through extensive simulations including comparisons against state-of-the-art arbitration schemes. The results show that FARRE is able to maintain or exceed the input fairness thresholds, and improves system performance over standard fair scheduling policies such as round-robin; the performance improvement is 14% for lower fairness thresholds such as 0.5, and could even gain 5% performance for aggressive thresholds such as 0.9. Additionally, compared to the best performance optimization techniques, FARRE achieves 81% higher fairness.

Energy harvesting wireless sensor networks (EHWSNs) are useful for ambient monitoring, especially in hazardous and hard-to-reach environments, where sensor nodes sense and transmit data to a remote sink via multi-hop communication, enabling informed decision making. Accurate decisions require a global snapshot that comprises environmental parameters simultaneously sensed from all nodes. To realize an efficient snapshot collection scheme, all nodes should send an equal number of messages where each message contributes to the snapshot; excessive messages from some nodes are ineffective and in-turn increase the network traffic and contribute to the wasteful work done by nodes. Reducing such ineffectual messages is challenging due to the variable ambient energy supply at nodes, network congestion, limited information about the overall state of the system and varying node distances from the sink. To achieve this objective, we introduce WaWoR, a novel distributed system where nodes dynamically decide when to sense and transmit snapshot messages based on their physical locations, energy availability and perceived network congestion. WaWoR outperforms two theoretical hypothetical baselines and two state-of-the-art-systems. It captures [90−175]% of the snapshots compared to the theoretical baselines and [1.02 − 4.26]× more snapshots than state-of-the-art systems. Additionally, it reduces wasted messages, which further reduces the total energy consumption by [1.6 − 9.9]×.

Polar codes have hitherto been used in the control plane of 5G-NR systems. However, in line with other contemporary works, we propose a novel use of them in the data plane by leveraging their natural property: different bit positions suffer from different degrees of errors. The idea is to map different components of web pages to different bit positions (based on their priority). We evaluate our approach for web page transmission over a wireless link that traditionally uses TCP and demonstrate benefits for image and video-based web pages. For an image-based web page, there is a 47.96% to 81.12% gain in performance, while the received image quality score varies between 0.97 and 0.99. We observe up to a 63.56% gain in performance for web pages with embedded videos with only a 7.45% loss in the received video quality.

Energy harvesting devices (EHDs) are becoming extremely prevalent in remote and hazardous environments. They sense the ambient parameters and compute some statistics on them, which are then sent to a remote server. Due to the resource-constrained nature of EHDs, it is challenging to perform exact computations on streaming data; however, if we are willing to tolerate a slight amount of inaccuracy, we can leverage the power of sketching algorithms to provide quick answers with significantly lower energy consumption.

With the advent of 5G and M2M architectures, energy harvesting devices are expected to become far more prevalent. Such devices harvest energy from ambient sources such as solar energy or vibration energy (from machines) and use it for sensing the environmental parameters and further processing them. Given that the rate of energy consumption is more than the rate of energy production, it is necessary to frequently halt the processor and accumulate energy from the environment. During this period it is mandatory to take a checkpoint to avoid the loss of data. State of the art algorithms use software based methods that extensively rely on compiler analyses. In this paper, we provide the first formal model for such systems, and show that we can arrive at an optimal check-pointing schedule using a quadratically constrained linear program (QCLP) solver. Using this as a baseline, we show that existing algorithms for checkpointing significantly underperform. Furthermore, we prove and demonstrate that when we have a relatively constant energy source, a greedy algorithm provides an optimal solution. To model more complex situations where the energy varies, we create a novel checkpointing algorithm that adapts itself according to the ambient energy. We obtain a speedup of 2 - 5× over the nearest competing approach, and we are within 3 - 8% of the optimal solution in the general case where the ambient energy exhibits variations.

Researchers have proposed numerous consistency models in distributed systems that offer higher performance than classical sequential consistency (SC). Even though these models do not guarantee sequential consistency; they either behave like an SC model under certain restrictive scenarios, or ensure SC behavior for a part of the system. We propose a different line of thinking where we try to accurately estimate the number of SC violations, and then try to adapt our system to optimally tradeoff performance, resource usage, and the number of SC violations. In this paper, we propose a generic theoretical model that can be used to analyze systems that are comprised of multiple sub-domains - each sequentially consistent. It is validated with real world measurements. Next, we use this model to propose a new form of consistency called social consistency, where socially connected users perceive an SC execution, whereas the rest of the users need not. We create a prototype social network application and implement it on the Cassandra key-value store. We show that our system has 2.4× more throughput than Cassandra and provides 37% better quality-of-experience.