An Improved LLF Scheduling for Reducing Maximum Heap Memory Consumption by Considering Laxity Time

Machigashira, Yuki; Nakata, Atsuo

doi:10.1109/tase.2018.00027

Cited by 5 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, we insert instrumentation into the memory allocation/deallocation functions in the standard libraries, and obtain 18 return S its parameters and return value. The reason is that the memory is allocated by some standard library functions [1,46], e.g., malloc, calloc, realloc, and new. On the other hand, the program may also free the memory using the standard library function such as free and delete.…”

Section: Memory Usage Operationsmentioning

confidence: 99%

“…Dynamic Analysis. Yuku et al [46] proposes an improved realtime scheduling algorithm to reduce maximal heap memory consumption by controlling multitask scheduling. Different from Mem-Lock, this technique aims at reducing memory consumption by dynamic online scheduling while MemLock is to find memory consumption bugs.…”

Section: Related Workmentioning

confidence: 99%

“…Existing detection techniques for memory consumption bugs usually use domain-or implementation-specific heuristics or rules [15,24,46,70,79]. For example, Radmin [24] learns and executes multiple probabilistic finite automata, and then confines the resource usage of target programs to the learned automata and detects resource usage anomalies at their early stages.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MemLock

Cheng

Wang

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

View full text Add to dashboard Cite

Uncontrolled memory consumption is a kind of critical software security weaknesses. It can also become a security-critical vulnerability when attackers can take control of the input to consume a large amount of memory and launch a Denial-of-Service attack. However, detecting such vulnerability is challenging, as the stateof-the-art fuzzing techniques focus on the code coverage but not memory consumption. To this end, we propose a memory usage guided fuzzing technique, named MemLock, to generate the excessive memory consumption inputs and trigger uncontrolled memory consumption bugs. The fuzzing process is guided with memory consumption information so that our approach is general and does not require any domain knowledge. We perform a thorough evaluation for MemLock on 14 widely-used real-world programs. Our experiment results show that MemLock substantially outperforms the state-of-the-art fuzzing techniques, including AFL, AFLfast, PerfFuzz, FairFuzz, Angora and QSYM, in discovering memory consumption bugs. During the experiments, we discovered many previously unknown memory consumption bugs and received 15 new CVEs. CCS CONCEPTS• Security and privacy → Software security engineering.

show abstract

Section: Memory Usage Operationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MemLock

Cheng

Wang

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

View full text Add to dashboard Cite

show abstract

“…Therefore, the diversity characteristics of IoT applications bring many challenges to EN. Especially, how to guarantee the QoS of delay-sensitive services is a hot research topic, and in recent years, many literatures have proposed some effective solutions, mainly including multitask scheduling algorithms [2][3][4][5][6][7][8] and collaborative computing algorithms [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the authors propose different scheduling strategies for different tasks based on VxWorks in an embedded realtime operating system area. In order to reduce memory consumption in real-time multitasking, [4] proposed an improved real-time scheduling algorithm based on Least Laxity First (LLF) to reduce maximal heap memory consumption by controlling multitask scheduling, without modifying the processing order of each task. [5] studied an extension of the completely fair scheduler (CFS) to support cooperative multitasking with time-sharing for heterogeneous processing elements.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multitask Scheduling and Distributed Collaborative Computing Method of Edge Nodes in the Internet of Things

Wang

Tang

Zhu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, we propose a novel multitask scheduling and distributed collaborative computing method for quality of service (QoS) guaranteed delay-sensitive services in the Internet of Things (IoT). First, we propose a multilevel scheduling framework combining the process and thread scheduling for reducing the processing delay of multitype services of a single edge node in IoT, where a preemptive static priority process scheduling algorithm is adopted for different types of services and a dynamic priority-based thread scheduling algorithm is proposed for the same type of services with high concurrency. Furthermore, for reducing the processing delay of computation-intensive services, we propose a distributed task offloading algorithm based on a multiple 0-1 knapsack model with value limitation with the collaboration of multiple edge nodes to minimize the processing delay. Simulation results show that the proposed method can significantly reduce not only the scheduling delay of a large number of time-sensitive services in single edge node but also the process delay of computation-intensive service collaborated by multiple edge nodes.

show abstract

Proposing ε-greedy Reinforcement Learning Technique to Self-Optimize Memory Controllers

Ray

2021

2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC)

View full text Add to dashboard Cite

An Improved LLF Scheduling for Reducing Maximum Heap Memory Consumption by Considering Laxity Time

Cited by 5 publications

References 4 publications

MemLock

MemLock

A Novel Multitask Scheduling and Distributed Collaborative Computing Method of Edge Nodes in the Internet of Things

Proposing ε-greedy Reinforcement Learning Technique to Self-Optimize Memory Controllers

Contact Info

Product

Resources

About