Response Time Analysis of Messages in Controller Area Network: A Review

Abstract

This paper reviews the research work done on the response time analysis of messages in controller area network (CAN) from the time CAN specification was submitted for standardization (1990) and became a standard (1993) up to the present (2012). Such research includes the worst-case response time analysis which is deterministic and probabilistic response time analysis which is stochastic. A detailed view on both types of analyses is presented here. In addition to these analyses, there has been research on statistical analysis of controller area network message response times.

Investigation the Effect of the Distribution of Time Slots on the Characteristics of a Network With Random Multiple Access

“…Научным сообществом выполняется множество научно-исследовательских работ, посвященных изучению влияния параметров алгоритмов доступа на вероятностные и временные характеристики передачи данных в информационно-управляющих сетях [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. В работе [5] изучается KNX-технология, в [6][7][8][9][10] CAN-шина, DeviceNet [11], в [12][13][14][15][16][17][18][19][20] выполняется анализ характеристик сетей с адаптивным протоколом, в частности, сети LonWorks с исследуемым в работе алгоритмом доступа. Работы посвящены изучению сетевых характеристик и созданию рекомендаций по выбору протокольных параметров алгоритмов доступа для обеспечения малой временной задержки передачи с требуемым уровнем надёжности доставки данных.…”

Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks

“…A recent overview of response-time analysis techniques for CAN is provided in [18], for both the deterministic and probabilistic cases. Although these previous works have examined probabilistic (bursty/sporadic/intermittent) errors and also deterministic errors within combined reliability and schedulability analysis frameworks, as noted in [1,8,18], one complication of some of these methods and of the use of probabilistic models in general is the potential complexity of implementation. Typically, an iterative procedure is needed to produce either a probability distribution of response times or a breakdown reliability beyond which point one or more tasks or messages will miss a deadline.…”

“…A second drawback in almost all previous analysis is an implicit assumption that each and every error arrival impacts the schedule by inducing a retransmission of a worst-case frame [18]. This is pessimistic, especially for CAN networks which have early error detection and signaling.…”

“…For bursts of errors, a more likely scenario seems that the first error in the burst sequence induces a frame retransmission, where subsequent errors in the burst sequence delay its starting time until the burst subsides. This is not taken into account in [1] or [8], or in any of the methods described in [18]. In the current work, efforts are made to improve the analysis in an attempt to address this issue, whilst retaining most aspects of the simplicity of application.…”

“…These assumptions are such that all errors are detected and that all messages which fail due to an error are immediately rescheduled using their original priority or deadline. Although the worst-case error load tolerable for a given message can be calculated exactly using sensitivity analysis combined with an exact test like responsetime analysis [18,23] or the processor demand approach [7], both would require an application of an exact schedulability analysis. Since the exact analysis is known to be coNPcomplete in both cases [29,30], simple bounds which may be easily manipulated algebraically are instead sought.…”