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1. Threshold Based Load Balancing Algorithm in Cloud Computing

   https://doi.org/10.1109/JCC56315.2022.00011  

   Chowdhury, Shusmoy; Katangur, Ajay (August 2022, 2022 IEEE International Conference on Joint Cloud Computing (JCC))

   Cloud computing has become an emerging trend for the software industry with the requirement of large infrastructure and resources. The future success of cloud computing depends on the effectiveness of instantiation of the infrastructure and utilization of available resources. Load Balancing ensures the fulfillment of these conditions to improve the cloud environment for the users. Load Balancing dynamically distributes the workload among the nodes in such a way that no single resource is either overwhelmed with tasks or underutilized. In this paper we propose a threshold based load balancing algorithm to ensure the equal distribution of the workload among the nodes. The main objective of the algorithms is to stop the VMs in the cloud being overloaded with tasks or being idle for lack allocation of tasks, when there are active tasks. We have simulated our proposed algorithm in the Cloudanalyst simulator with real world data scenarios. Simulation results shows that our proposed threshold based algorithm can provide a better response time for the task/requests and data processing time for the datacenters compared to the existing algorithms such as First Come First Serve (FCFS), Round Robin(RR) and Equally Spread Current Execution Load Balancing algorithm(ESCELB). 

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2. Optimal Admission Policy in a Cloud Data Center with Priority and Non-Priority Tasks

   Ni, Wenlong; Zhang, Yuhong; Li, Wei (May 2025, Springer Nature Switzerland AG 2025)

   This paper studies a cloud datacenter (DC) consisting of two types of tasks with different priority levels. While non-priority tasks generally request the use of a single virtual machine (VM), priority tasks may utilize multiple available VMs to accelerate processing. We focus on determining whether to accept or reject non-priority tasks to maximize overall system benefits. By formulating the problem as a stochastic dynamic program, it is verified that the best approach for handling nonpriority tasks adheres to a control-limit framework. Both experimental outcomes and numerical evaluations highlight the efficacy of the proposed method, leading to the identification of the optimal threshold. The key contribution of this paper is the development of a stochastic dynamic program for DC resource management and the explicit derivation of an optimal control-limit policy. Both value iteration and linear programming methods are utilized to solve optimization problems. These results offer essential understanding for assessing the performance of various DC models, optimizing both rewards and resources efficiently. 

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3. Data-priority Aware Fair Task Scheduling for Stream Processing at the Edge

   Akram, Faiza; Kang, Peng; Lama, Palden; Khan, Samee U (June 2024, IEEE)

   Real-time data stream processing at the edge is crucial for time-sensitive tasks within large-scale IoT systems. Task scheduling plays a key role in managing the Quality of Service (QoS), necessitating a prioritization system to distinguish between high and low-priority tasks, thus ensuring efficient data processing on edge nodes. Existing scheduling algorithms rigidly prioritize tasks deemed as high-priority, often at the expense of fairness and overall system efficiency. In this paper, we propose a Priority-aware Fair Task Scheduling (FTS-Hybrid) algorithm that addresses these challenges by managing priority based task execution in a controlled manner. Our task scheduling algorithm streamlines resource utilization and enhances system responsiveness, contributing to low latency and high throughput, outperforming competing techniques including First-Come-FirstServe (FCFS), Round Robin (RR), and Priority Scheduling (PS). We implemented FTS-Hybrid on Apache Storm and evaluated its performance using an open-source real-time IoT benchmark (RIoTBench). Experimental results show that the FTS-Hybrid algorithm reduces task execution latency by 24%, 31%, and 26% compared with FCFS, RR, and PS, respectively, by strategically mitigating queuing delays under dynamic workload conditions. 

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4. Hiding DRAM Refresh Overhead in Real-Time Cyclic Executives

   Pan, Xing; Mueller, Frank (December 2019, Real-Time Systems Symposium (RTSS))

   eal-time systems with hard timing constrains require known upper bounds on each task’s worst-case execution time (WCET) to determine if all deadlines can be met. One challenge in predictable execution is that Dynamic Random Access Memory (DRAM) cells must be refreshed periodically to maintain data validity, yet memory remains blocked during refresh, which results in overly pessimistic WCET bounds. This work contributes “Colored Refresh” to hide DRAM refresh overhead while preserving real-time schedulability for cyclic executives, which are widely used in highly critical systems. Colored Refresh partitions DRAM memory at rank granularity such that refreshes rotate round-robin from rank to rank. Real-time tasks are assigned different ranks via colored memory allocation. By cooperatively scheduling real-time tasks and refresh operations, memory requests no longer suffer from refresh interference. This reduces memory access latencies for tasks irrespective of DRAM density and size. Hence, Colored Refresh reduces a task’s WCET and makes its execution more predictable. 

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5. Efficient OpenCL Accelerators for Canny Edge Detection Algorithm on a CPU-FPGA Platform

   https://doi.org/10.1109/ReConFig48160.2019.8994769  

   Rahamneh, Samah; Sawalha, Lina (December 2019, International Conference on ReConFigurable Computing and FPGAs (ReConFig))

   The processing demands of current and emerging applications, such as image/video processing, are increasing due to the deluge of data, generated by mobile and edge devices. This raises challenges for a vast range of computing systems, starting from smart-phones and reaching cloud and data centers. Heterogeneous computing demonstrates its ability as an efficient computing model due to its capability to adapt to various workload requirements. Field programmable gate arrays (FPGAs) provide power and performance benefits and have been used in many application domains from embedded systems to the cloud. In this paper, we used a closely coupled CPU-FPGA heterogeneous system to accelerate a sliding window based image processing algorithm, Canny edge detector. We accelerated Canny using two different implementations: Code partitioned and data partitioned. In the data partitioned implementation, we proposed a weighted round-robin based algorithm that partitions input images and distributes the load between the CPU and the FPGA based on latency. The paper also compares the performance of the proposed accelerators with separate CPU and FPGA implementations. Using our hybrid CPU-FPGA based algorithm, we achieved a speedup of up to 4.8× over a CPU-only and up to 2.1× over a FPGA-only implementations. Moreover, the estimated total energy consumption of our algorithm is more efficient than a CPU-only implementation. Our results show a significant reduction in energy-delay product (EDP) compared to the CPU-only implementation, and comparable EDP results to the FPGA-only implementation. 

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